Book contents
- Frontmatter
- Contents
- List of figures
- List of tables
- List of boxes
- List of screenshots
- Preface to the third edition
- Acknowledgements
- 1 Introduction
- 2 Mathematical and statistical foundations
- 3 A brief overview of the classical linear regression model
- 4 Further development and analysis of the classical linear regression model
- 5 Classical linear regression model assumptions and diagnostic tests
- 6 Univariate time series modelling and forecasting
- 7 Multivariate models
- 8 Modelling long-run relationships in finance
- 9 Modelling volatility and correlation
- 10 Switching models
- 11 Panel data
- 12 Limited dependent variable models
- 13 Simulation methods
- 14 Conducting empirical research or doing a project or dissertation in finance
- Appendix 1 Sources of data used in this book
- Appendix 2 Tables of statistical distributions
- Glossary
- References
- Index
13 - Simulation methods
- Frontmatter
- Contents
- List of figures
- List of tables
- List of boxes
- List of screenshots
- Preface to the third edition
- Acknowledgements
- 1 Introduction
- 2 Mathematical and statistical foundations
- 3 A brief overview of the classical linear regression model
- 4 Further development and analysis of the classical linear regression model
- 5 Classical linear regression model assumptions and diagnostic tests
- 6 Univariate time series modelling and forecasting
- 7 Multivariate models
- 8 Modelling long-run relationships in finance
- 9 Modelling volatility and correlation
- 10 Switching models
- 11 Panel data
- 12 Limited dependent variable models
- 13 Simulation methods
- 14 Conducting empirical research or doing a project or dissertation in finance
- Appendix 1 Sources of data used in this book
- Appendix 2 Tables of statistical distributions
- Glossary
- References
- Index
Summary
Learning outcomes
In this chapter, you will learn how to
• Design simulation frameworks to solve a variety of problems in finance
• Explain the difference between pure simulation and bootstrapping
• Describe the various techniques available for reducing Monte Carlo sampling variability
• Implement a simulation analysis in EViews
Motivations
There are numerous situations, in finance and in econometrics, where the researcher has essentially no idea what is going to happen! To offer one illustration, in the context of complex financial risk measurement models for portfolios containing large numbers of assets whose movements are dependent on one another, it is not always clear what will be the effect of changing circumstances. For example, following full European Monetary Union (EMU) and the replacement of member currencies with the euro, it is widely believed that European financial markets have become more integrated, leading the correlation between movements in their equity markets to rise. What would be the effect on the properties of a portfolio containing equities of several European countries if correlations between the markets rose to 99%? Clearly, it is probably not possible to be able to answer such a question using actual historical data alone, since the event (a correlation of 99%) has not yet happened.
The practice of econometrics is made difficult by the behaviour of series and inter-relationships between them that render model assumptions at best questionable.
- Type
- Chapter
- Information
- Introductory Econometrics for Finance , pp. 591 - 625Publisher: Cambridge University PressPrint publication year: 2014